{"id":"W3094152559","doi":"10.2196/23173","title":"A Data Visualization and Dissemination Resource to Support HIV Prevention and Care at the Local Level: Analysis and Uses of the AIDSVu Public Data Resource","year":2020,"lang":"en","type":"article","venue":"Journal of Medical Internet Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":195,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Allergy and Infectious Diseases; Center for AIDS Research, University of Washington; Petroleum Technology Alliance Canada; Emory University; Gilead Sciences","keywords":"Infographic; Population; Data visualization; Census; Public health; Data science; Computer science; Resource (disambiguation); American Community Survey; Social media; Geocoding; Geography; Medicine; Visualization; World Wide Web; Environmental health; Data mining; Cartography","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.005357036,0.0000955872,0.0003319657,0.0002364379,0.00007842835,0.00009615609,0.001168824,0.00008669584,0.0002517766],"category_scores_gemma":[0.01274427,0.00005348345,0.00004468096,0.000876988,0.0005647438,0.0002355534,0.003910716,0.0004491057,0.000001690826],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006248194,"about_ca_system_score_gemma":0.000249387,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005080317,"about_ca_topic_score_gemma":0.0007004429,"domain_scores_codex":[0.9956028,0.0008690397,0.0005340455,0.0003651391,0.002435057,0.0001939478],"domain_scores_gemma":[0.9972141,0.0007788228,0.0002190118,0.0008126495,0.0004119806,0.0005633805],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001164907,0.0001965701,0.2376791,0.0006457187,0.001024078,0.0001173784,0.004809942,0.000003489536,0.0003369645,0.0001302826,0.5601872,0.1937045],"study_design_scores_gemma":[0.00225918,0.001933822,0.3257355,0.001707971,0.001212079,0.0004010988,0.01517806,0.06250828,0.0007437688,0.00001919169,0.588092,0.0002091104],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9192041,0.002304785,0.01126935,0.06545924,0.00002828216,0.0005068251,0.001043834,0.00001051207,0.0001730617],"genre_scores_gemma":[0.9978964,0.0002450823,0.0000697408,0.0003467521,0.0001400472,0.000001997227,0.0008197926,0.00001282294,0.0004673837],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1934953,"threshold_uncertainty_score":0.9955718,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.18371200007225,"score_gpt":0.4728071293570879,"score_spread":0.2890951292848379,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}